CAJ | 학술논문

借助于半罚函数和产生工作集的识别函数以及模松弛SQP算法思想,建立了求解带等式及不等式约束优化的一个新算法.每次迭代中,算法的搜索方向由一个简化的二次规划子问题及一个简化的线性方程组产生.算法在不包含严格互补性的温和条件下具有全局收敛性和超线性收敛性.最后给出了算法初步的数值试验报告.
차조우반벌함수화산생공작집적식별함수이급모송이SQP산법사상,건립료구해대등식급불등식약속우화적일개신산법.매차질대중,산법적수색방향유일개간화적이차규화자문제급일개간화적선성방정조산생.산법재불포함엄격호보성적온화조건하구유전국수렴성화초선성수렴성.최후급출료산법초보적수치시험보고.
Based on a semi-penalty function and an identification function (for yielding a “working set”), as well as the norm-relaxed SQP idea, this paper proposed a new algorithm for solving a kind of optimization problems with nonlinear equality and inequality constraints. At each iteration, to yield the search directions the algorithm solves only one reduced quadratic program (QP) subproblem and a reduced system of linear equations. The proposed algorithm possesses global convergence and superlinear convergence under some mild assumptions without the strictly complementarity. Finally,some elementary numerical experiments are reported.